Data Science with R and Python
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F22%3A10249847" target="_blank" >RIV/61989100:27740/22:10249847 - isvavai.cz</a>
Výsledek na webu
<a href="https://events.it4i.cz/event/138/" target="_blank" >https://events.it4i.cz/event/138/</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Data Science with R and Python
Popis výsledku v původním jazyce
The R part of the course was focused on presenting the basics of exploratory data analysis in R, as well as presentation of the findings through visualization, and basics of statistical/machine learning modelling. The course covered the basic workflow of exploratory analysis using packages from the 'tidyverse' universe. These included packages for the loading of data, preprocessing data, basic data exploration, and visualization. In the second part, the basics of modelling in R starting with data preparation (missing data handling, one-hot enconding, etc.), model training, and model evaluation were introduced. In this part the main tools were packages 'caret' and 'xgboost'. The Python oriented part introduced essential data-scientific packages and demonstrated their usage with real world data analytic problems, and showed how to tackle such problems.
Název v anglickém jazyce
Data Science with R and Python
Popis výsledku anglicky
The R part of the course was focused on presenting the basics of exploratory data analysis in R, as well as presentation of the findings through visualization, and basics of statistical/machine learning modelling. The course covered the basic workflow of exploratory analysis using packages from the 'tidyverse' universe. These included packages for the loading of data, preprocessing data, basic data exploration, and visualization. In the second part, the basics of modelling in R starting with data preparation (missing data handling, one-hot enconding, etc.), model training, and model evaluation were introduced. In this part the main tools were packages 'caret' and 'xgboost'. The Python oriented part introduced essential data-scientific packages and demonstrated their usage with real world data analytic problems, and showed how to tackle such problems.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
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Návaznosti
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů